Daniel B. Neill, Ph.D.|
Associate Professor of Computer Science and Public
Associate Professor of Urban Analytics3
New York University
1Courant Institute of Mathematical Sciences, Department of
2Robert F. Wagner Graduate School of Public Service
3Center for Urban Science and Progress
E-mail: firstname.lastname @ nyu.edu
Very brief bio:
I am an Associate Professor of Computer Science and Public Service at New
York University's Courant Institute Department
of Computer Science and Robert F.
Wagner Graduate School of Public Service. I am also Associate
Professor of Urban Analytics at NYU's Center
for Urban Science and Progress. Previously, I was Associate Professor
of Information Systems in the Heinz
College at Carnegie Mellon
University, where I was the H.J. Heinz III College Dean's Career
Development Professor and Director of the Event and Pattern Detection
Laboratory. I received my Ph.D. in Computer Science from CMU in 2006.
Before that, I received my B.S.E. from Duke
University, M.Phil. from Cambridge
University, and M.S. from Carnegie Mellon.
Daniel B. Neill is Associate Professor of Computer Science
and Public Service at NYU’s Courant Institute Department of Computer Science
and Robert F. Wagner Graduate School of Public Service, and
Associate Professor of Urban Analytics at NYU’s Center for Urban Science
and Progress. He was previously a tenured faculty member at Carnegie
Mellon University’s Heinz College, where he was the Dean’s Career
Development Professor, Associate Professor of Information Systems, and
Director of the Event and Pattern Detection Laboratory. He received his
M.Phil. from Cambridge University and his M.S. and Ph.D. in Computer
Science from Carnegie Mellon University. Dr. Neill’s research focuses on
developing new methods for machine learning and event detection in massive
and complex datasets, with applications ranging from medicine and public
health to law enforcement and urban analytics. He works closely with
organizations including public health, police departments, hospitals, and
city leaders to create and deploy data-driven tools and systems to improve
the quality of public health, safety, and security, for example, through
the early detection of disease outbreaks and through predicting and
preventing hot-spots of violent crime. He is also the Associate Editor of
four journals (IEEE Intelligent Systems, Decision Sciences, Security
Informatics, and ACM Transactions on Management Information Systems). He
was the recipient of an NSF CAREER award and an NSF Graduate Research
Fellowship, and was named one of the "top ten artificial intelligence
researchers to watch" by IEEE Intelligent Systems.
Research: My research is focused on novel statistical and
computational methods for discovery of emerging events and other relevant
patterns in complex and massive datasets, applied to real-world policy
problems ranging from medicine and public health to law enforcement and
security. Application areas include disease surveillance (e.g.,
using electronically available public health data such as hospital visits
and medication sales to automatically identify and characterize emerging
outbreaks), law enforcement (e.g., detection and prediction of
crime patterns using offense reports and 911 calls), health care
(e.g., detecting anomalous patterns of care which significantly impact
patient outcomes), and urban analytics (e.g., helping city
governments to predict and proactively respond to emerging patterns of
Selected publications by
Google Scholar page
CMU homepage (old)
CMU Event and Pattern Detection Lab
Which projects am I most excited about these days? So glad you
asked! In no particular order:
A detailed statement of my research
interests (last updated
October 2017) can be found here, and
additional details can be found on my (old) EPD Lab project page.
- Pre-syndromic surveillance is a new way of thinking about
public health and disease surveillance, using unstructured data to detect novel
bio-threats and other emerging patterns of interest to public health.
- Algorithmic fairness, particularly, how we can audit black-box
algorithms to identify and correct systematic biases in risk prediction.
- Discovering heterogeneous treatment effects in both randomized
experiments and observational data, e.g., identifying patterns of care
that impact patient health outcomes, and analyzing the impacts of exposure
to poor housing conditions on health.
- Detecting patterns in massive, complex data such as images,
text, and social media, with applications including civil unrest prediction, rare disease
outbreak detection, and discovery of emerging patterns of human rights
- Automating the detection of natural experiments, including
regression discontinuities, difference-in-differences, and instrumental variables,
for causal inference.
- Modeling and detecting patterns in complex urban data,
two of my favorite methodological approaches. Scalable Gaussian
processes enable accurate modeling and prediction in correlated
spatio-temporal data. Then, given this model of "typical" system
behavior, subset scanning can reliably detect subtle deviations by
identifying subsets of the data that are collectively anomalous.
- Predictive policing, including a randomized field trial to
analyze the impact of targeted hot-spot patrolling on violent crime, and
other applications of machine learning for law enforcement and criminal justice.
- Opioid abuse and overdose surveillance, using data from
prescription drug monitoring programs, law enforcement, and county medical
- Continuing to extend our fast subset scan methodology for
pattern detection, e.g., to massive graphs, multidimensional data, and
irregularly-shaped spatial clusters.
Our pre-syndromic surveillance project was selected as the runner-up in the Department of Homeland Security's Hidden Signals Challenge, a nationwide system design competition which focuses on detecting emerging bio-threats in real time. Here is the link to the winner announcement.
I am guest co-editor of a special issue of GeoInformatica on "Analytics for Local Events and News". Submissions are due August 15th. Please feel free to distribute this call for papers. Note that all papers should be submitted through the Springer GeoInformatica website.
Our rodent prevention work was recently featured in an article on CityLab.
According to the article, "The city of Chicago is still running Neill's predictive analytics approach and has
touted that it's 20 percent more effective than the traditional method of baiting rats after they've been
Our paper on Semantic Scan: Detecting Subtle, Spatially Localized Events in Text Streams was named the
winner of the Yelp Dataset
Challenge. Our approach for identifying emerging topics can be
used both for public health (detecting "novel" outbreaks with rare or previously unseen symptom patterns) as well as identifying emerging regional
business trends. Thanks to both Yelp and CMU for their very
nice press coverage of this work!
Our crime prediction work with the Pittsburgh Bureau of Police was featured in an editorial in the 30 Sep 2016 issue of
Our comprehensive review article, "Youth violence: what we know and what we need to know", was featured in
a press release by the
American Psychological Association. The article was published in the January 2016 issue of the APA's
flagship journal, American Psychologist, and is available here.
I gratefully acknowledge funding support from the National Science
Foundation, grants IIS-0916345, IIS-0911032, and IIS-0953330, as well as a
UPMC Healthcare Technology Innovation Grant, funding from the John D. and
Catherine T. MacArthur Foundation and Richard King Mellon Foundation, and
a gift from the Disruptive Health Technology Institute. Any opinions,
findings, and conclusions or recommendations expressed in this material
are those of the author(s) and do not necessarily reflect the views of the
National Science Foundation, UPMC, DHTI, Richard King Mellon Foundation,
or MacArthur Foundation.
Last updated: 7/27/2018